Applied Unsupervised Learning with R

Chapter 5

Data Comparison Methods

Learning Objectives

By the end of this chapter, you will be able to:

• Create hashes of data
• Create image signatures
• Compare image datasets
• Perform factor analysis to isolate latent variables
• Compare surveys and other datasets using factor analysis

In this chapter, we will have a look at different data comparison methods.

Introduction

Unsupervised learning is concerned with analyzing the structure of data to draw useful conclusions. In this chapter, we will examine methods that enable us to use the structure of data to compare datasets. The major methods we will look at are hash functions, analytic signatures, and latent variable models.

Hash Functions

Imagine that you want to send an R script to your ...